Overview

Dataset statistics

Number of variables38
Number of observations1460
Missing cells348
Missing cells (%)0.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory433.6 KiB
Average record size in memory304.1 B

Variable types

Numeric30
Categorical8

Warnings

OverallQual is highly correlated with YearBuilt and 8 other fieldsHigh correlation
YearBuilt is highly correlated with OverallQual and 4 other fieldsHigh correlation
YearRemodAdd is highly correlated with OverallQual and 3 other fieldsHigh correlation
BsmtFinSF1 is highly correlated with TotalBsmtSF and 1 other fieldsHigh correlation
TotalBsmtSF is highly correlated with OverallQual and 3 other fieldsHigh correlation
1stFlrSF is highly correlated with TotalBsmtSF and 2 other fieldsHigh correlation
2ndFlrSF is highly correlated with GrLivArea and 3 other fieldsHigh correlation
GrLivArea is highly correlated with OverallQual and 6 other fieldsHigh correlation
BsmtFullBath is highly correlated with BsmtFinSF1High correlation
FullBath is highly correlated with OverallQual and 3 other fieldsHigh correlation
HalfBath is highly correlated with 2ndFlrSFHigh correlation
BedroomAbvGr is highly correlated with 2ndFlrSF and 2 other fieldsHigh correlation
TotRmsAbvGrd is highly correlated with 2ndFlrSF and 4 other fieldsHigh correlation
GarageYrBlt is highly correlated with OverallQual and 4 other fieldsHigh correlation
GarageCars is highly correlated with OverallQual and 4 other fieldsHigh correlation
GarageArea is highly correlated with OverallQual and 3 other fieldsHigh correlation
SalePrice is highly correlated with OverallQual and 9 other fieldsHigh correlation
LotFrontage is highly correlated with LotAreaHigh correlation
LotArea is highly correlated with LotFrontageHigh correlation
OverallQual is highly correlated with YearBuilt and 7 other fieldsHigh correlation
YearBuilt is highly correlated with OverallQual and 6 other fieldsHigh correlation
YearRemodAdd is highly correlated with OverallQual and 3 other fieldsHigh correlation
BsmtFinSF1 is highly correlated with BsmtUnfSF and 1 other fieldsHigh correlation
BsmtUnfSF is highly correlated with BsmtFinSF1High correlation
TotalBsmtSF is highly correlated with 1stFlrSF and 1 other fieldsHigh correlation
1stFlrSF is highly correlated with TotalBsmtSF and 1 other fieldsHigh correlation
2ndFlrSF is highly correlated with GrLivArea and 3 other fieldsHigh correlation
GrLivArea is highly correlated with OverallQual and 6 other fieldsHigh correlation
BsmtFullBath is highly correlated with BsmtFinSF1High correlation
FullBath is highly correlated with OverallQual and 6 other fieldsHigh correlation
HalfBath is highly correlated with 2ndFlrSFHigh correlation
BedroomAbvGr is highly correlated with 2ndFlrSF and 2 other fieldsHigh correlation
TotRmsAbvGrd is highly correlated with 2ndFlrSF and 4 other fieldsHigh correlation
Fireplaces is highly correlated with SalePriceHigh correlation
GarageYrBlt is highly correlated with OverallQual and 6 other fieldsHigh correlation
GarageCars is highly correlated with OverallQual and 6 other fieldsHigh correlation
GarageArea is highly correlated with OverallQual and 4 other fieldsHigh correlation
SalePrice is highly correlated with OverallQual and 11 other fieldsHigh correlation
LotFrontage is highly correlated with LotAreaHigh correlation
LotArea is highly correlated with LotFrontageHigh correlation
OverallQual is highly correlated with YearBuilt and 3 other fieldsHigh correlation
YearBuilt is highly correlated with OverallQual and 2 other fieldsHigh correlation
YearRemodAdd is highly correlated with YearBuilt and 1 other fieldsHigh correlation
BsmtFinSF1 is highly correlated with BsmtFullBathHigh correlation
TotalBsmtSF is highly correlated with 1stFlrSFHigh correlation
1stFlrSF is highly correlated with TotalBsmtSFHigh correlation
2ndFlrSF is highly correlated with GrLivArea and 1 other fieldsHigh correlation
GrLivArea is highly correlated with 2ndFlrSF and 3 other fieldsHigh correlation
BsmtFullBath is highly correlated with BsmtFinSF1High correlation
FullBath is highly correlated with OverallQual and 2 other fieldsHigh correlation
HalfBath is highly correlated with 2ndFlrSFHigh correlation
BedroomAbvGr is highly correlated with TotRmsAbvGrdHigh correlation
TotRmsAbvGrd is highly correlated with GrLivArea and 1 other fieldsHigh correlation
GarageYrBlt is highly correlated with YearBuilt and 2 other fieldsHigh correlation
GarageCars is highly correlated with OverallQual and 3 other fieldsHigh correlation
GarageArea is highly correlated with GarageCarsHigh correlation
SalePrice is highly correlated with OverallQual and 3 other fieldsHigh correlation
Fireplaces is highly correlated with 1stFlrSF and 1 other fieldsHigh correlation
BsmtFinSF1 is highly correlated with GarageArea and 7 other fieldsHigh correlation
EnclosedPorch is highly correlated with PoolAreaHigh correlation
2ndFlrSF is highly correlated with OverallQual and 8 other fieldsHigh correlation
GarageArea is highly correlated with BsmtFinSF1 and 7 other fieldsHigh correlation
OverallQual is highly correlated with 2ndFlrSF and 10 other fieldsHigh correlation
1stFlrSF is highly correlated with Fireplaces and 10 other fieldsHigh correlation
KitchenAbvGr is highly correlated with MSSubClassHigh correlation
YearBuilt is highly correlated with 2ndFlrSF and 9 other fieldsHigh correlation
OverallCond is highly correlated with OverallQualHigh correlation
BsmtFullBath is highly correlated with BsmtFinSF1 and 2 other fieldsHigh correlation
OpenPorchSF is highly correlated with BsmtFinSF1 and 1 other fieldsHigh correlation
GarageYrBlt is highly correlated with YearBuilt and 4 other fieldsHigh correlation
GarageCars is highly correlated with GarageArea and 7 other fieldsHigh correlation
SalePrice is highly correlated with BsmtFinSF1 and 12 other fieldsHigh correlation
LotFrontage is highly correlated with BsmtFinSF1 and 3 other fieldsHigh correlation
MasVnrArea is highly correlated with 2ndFlrSF and 3 other fieldsHigh correlation
BsmtUnfSF is highly correlated with 1stFlrSF and 1 other fieldsHigh correlation
FullBath is highly correlated with 2ndFlrSF and 10 other fieldsHigh correlation
BedroomAbvGr is highly correlated with BsmtFullBath and 4 other fieldsHigh correlation
TotRmsAbvGrd is highly correlated with 2ndFlrSF and 6 other fieldsHigh correlation
GrLivArea is highly correlated with Fireplaces and 16 other fieldsHigh correlation
PoolArea is highly correlated with EnclosedPorch and 3 other fieldsHigh correlation
TotalBsmtSF is highly correlated with BsmtFinSF1 and 8 other fieldsHigh correlation
YearRemodAdd is highly correlated with YearBuilt and 2 other fieldsHigh correlation
HalfBath is highly correlated with 2ndFlrSF and 1 other fieldsHigh correlation
MSSubClass is highly correlated with 2ndFlrSF and 6 other fieldsHigh correlation
LotFrontage has 259 (17.7%) missing values Missing
GarageYrBlt has 81 (5.5%) missing values Missing
MiscVal is highly skewed (γ1 = 24.47679419) Skewed
Id is uniformly distributed Uniform
Id has unique values Unique
MasVnrArea has 861 (59.0%) zeros Zeros
BsmtFinSF1 has 467 (32.0%) zeros Zeros
BsmtFinSF2 has 1293 (88.6%) zeros Zeros
BsmtUnfSF has 118 (8.1%) zeros Zeros
TotalBsmtSF has 37 (2.5%) zeros Zeros
2ndFlrSF has 829 (56.8%) zeros Zeros
LowQualFinSF has 1434 (98.2%) zeros Zeros
GarageArea has 81 (5.5%) zeros Zeros
WoodDeckSF has 761 (52.1%) zeros Zeros
OpenPorchSF has 656 (44.9%) zeros Zeros
EnclosedPorch has 1252 (85.8%) zeros Zeros
3SsnPorch has 1436 (98.4%) zeros Zeros
ScreenPorch has 1344 (92.1%) zeros Zeros
PoolArea has 1453 (99.5%) zeros Zeros
MiscVal has 1408 (96.4%) zeros Zeros

Reproduction

Analysis started2022-01-26 14:14:15.637977
Analysis finished2022-01-26 14:17:58.501187
Duration3 minutes and 42.86 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

Id
Real number (ℝ≥0)

UNIFORM
UNIQUE

Distinct1460
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean730.5
Minimum1
Maximum1460
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2022-01-26T19:47:58.748833image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile73.95
Q1365.75
median730.5
Q31095.25
95-th percentile1387.05
Maximum1460
Range1459
Interquartile range (IQR)729.5

Descriptive statistics

Standard deviation421.6100094
Coefficient of variation (CV)0.577152648
Kurtosis-1.2
Mean730.5
Median Absolute Deviation (MAD)365
Skewness0
Sum1066530
Variance177755
MonotonicityStrictly increasing
2022-01-26T19:47:59.032822image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11
 
0.1%
9821
 
0.1%
9801
 
0.1%
9791
 
0.1%
9781
 
0.1%
9771
 
0.1%
9761
 
0.1%
9751
 
0.1%
9741
 
0.1%
9731
 
0.1%
Other values (1450)1450
99.3%
ValueCountFrequency (%)
11
0.1%
21
0.1%
31
0.1%
41
0.1%
51
0.1%
61
0.1%
71
0.1%
81
0.1%
91
0.1%
101
0.1%
ValueCountFrequency (%)
14601
0.1%
14591
0.1%
14581
0.1%
14571
0.1%
14561
0.1%
14551
0.1%
14541
0.1%
14531
0.1%
14521
0.1%
14511
0.1%

MSSubClass
Real number (ℝ≥0)

HIGH CORRELATION

Distinct15
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56.89726027
Minimum20
Maximum190
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2022-01-26T19:47:59.265820image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile20
Q120
median50
Q370
95-th percentile160
Maximum190
Range170
Interquartile range (IQR)50

Descriptive statistics

Standard deviation42.30057099
Coefficient of variation (CV)0.7434553226
Kurtosis1.580187965
Mean56.89726027
Median Absolute Deviation (MAD)30
Skewness1.407656747
Sum83070
Variance1789.338306
MonotonicityNot monotonic
2022-01-26T19:47:59.484125image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
20536
36.7%
60299
20.5%
50144
 
9.9%
12087
 
6.0%
3069
 
4.7%
16063
 
4.3%
7060
 
4.1%
8058
 
4.0%
9052
 
3.6%
19030
 
2.1%
Other values (5)62
 
4.2%
ValueCountFrequency (%)
20536
36.7%
3069
 
4.7%
404
 
0.3%
4512
 
0.8%
50144
 
9.9%
60299
20.5%
7060
 
4.1%
7516
 
1.1%
8058
 
4.0%
8520
 
1.4%
ValueCountFrequency (%)
19030
 
2.1%
18010
 
0.7%
16063
 
4.3%
12087
 
6.0%
9052
 
3.6%
8520
 
1.4%
8058
 
4.0%
7516
 
1.1%
7060
 
4.1%
60299
20.5%

LotFrontage
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct110
Distinct (%)9.2%
Missing259
Missing (%)17.7%
Infinite0
Infinite (%)0.0%
Mean70.04995837
Minimum21
Maximum313
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2022-01-26T19:47:59.749478image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile34
Q159
median69
Q380
95-th percentile107
Maximum313
Range292
Interquartile range (IQR)21

Descriptive statistics

Standard deviation24.28475177
Coefficient of variation (CV)0.3466776047
Kurtosis17.45286726
Mean70.04995837
Median Absolute Deviation (MAD)11
Skewness2.163569142
Sum84130
Variance589.7491687
MonotonicityNot monotonic
2022-01-26T19:48:00.027620image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60143
 
9.8%
7070
 
4.8%
8069
 
4.7%
5057
 
3.9%
7553
 
3.6%
6544
 
3.0%
8540
 
2.7%
7825
 
1.7%
9023
 
1.6%
2123
 
1.6%
Other values (100)654
44.8%
(Missing)259
 
17.7%
ValueCountFrequency (%)
2123
1.6%
2419
1.3%
306
 
0.4%
325
 
0.3%
331
 
0.1%
3410
0.7%
359
 
0.6%
366
 
0.4%
375
 
0.3%
381
 
0.1%
ValueCountFrequency (%)
3132
0.1%
1821
0.1%
1742
0.1%
1681
0.1%
1601
0.1%
1531
0.1%
1521
0.1%
1501
0.1%
1491
0.1%
1441
0.1%

LotArea
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct1073
Distinct (%)73.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10516.82808
Minimum1300
Maximum215245
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2022-01-26T19:48:00.293871image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1300
5-th percentile3311.7
Q17553.5
median9478.5
Q311601.5
95-th percentile17401.15
Maximum215245
Range213945
Interquartile range (IQR)4048

Descriptive statistics

Standard deviation9981.264932
Coefficient of variation (CV)0.949075601
Kurtosis203.243271
Mean10516.82808
Median Absolute Deviation (MAD)1998
Skewness12.20768785
Sum15354569
Variance99625649.65
MonotonicityNot monotonic
2022-01-26T19:48:00.632393image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
720025
 
1.7%
960024
 
1.6%
600017
 
1.2%
900014
 
1.0%
840014
 
1.0%
1080014
 
1.0%
168010
 
0.7%
75009
 
0.6%
91008
 
0.5%
81258
 
0.5%
Other values (1063)1317
90.2%
ValueCountFrequency (%)
13001
 
0.1%
14771
 
0.1%
14911
 
0.1%
15261
 
0.1%
15332
 
0.1%
15961
 
0.1%
168010
0.7%
18691
 
0.1%
18902
 
0.1%
19201
 
0.1%
ValueCountFrequency (%)
2152451
0.1%
1646601
0.1%
1590001
0.1%
1151491
0.1%
707611
0.1%
638871
0.1%
572001
0.1%
535041
0.1%
532271
0.1%
531071
0.1%

OverallQual
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct10
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.099315068
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2022-01-26T19:48:00.892396image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q15
median6
Q37
95-th percentile8
Maximum10
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.382996547
Coefficient of variation (CV)0.2267462053
Kurtosis0.09629277836
Mean6.099315068
Median Absolute Deviation (MAD)1
Skewness0.2169439278
Sum8905
Variance1.912679448
MonotonicityNot monotonic
2022-01-26T19:48:01.151875image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
5397
27.2%
6374
25.6%
7319
21.8%
8168
11.5%
4116
 
7.9%
943
 
2.9%
320
 
1.4%
1018
 
1.2%
23
 
0.2%
12
 
0.1%
ValueCountFrequency (%)
12
 
0.1%
23
 
0.2%
320
 
1.4%
4116
 
7.9%
5397
27.2%
6374
25.6%
7319
21.8%
8168
11.5%
943
 
2.9%
1018
 
1.2%
ValueCountFrequency (%)
1018
 
1.2%
943
 
2.9%
8168
11.5%
7319
21.8%
6374
25.6%
5397
27.2%
4116
 
7.9%
320
 
1.4%
23
 
0.2%
12
 
0.1%

OverallCond
Real number (ℝ≥0)

HIGH CORRELATION

Distinct9
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.575342466
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2022-01-26T19:48:01.381518image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q15
median5
Q36
95-th percentile8
Maximum9
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.112799337
Coefficient of variation (CV)0.1995930014
Kurtosis1.106413461
Mean5.575342466
Median Absolute Deviation (MAD)0
Skewness0.6930674725
Sum8140
Variance1.238322364
MonotonicityNot monotonic
2022-01-26T19:48:01.668301image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
5821
56.2%
6252
 
17.3%
7205
 
14.0%
872
 
4.9%
457
 
3.9%
325
 
1.7%
922
 
1.5%
25
 
0.3%
11
 
0.1%
ValueCountFrequency (%)
11
 
0.1%
25
 
0.3%
325
 
1.7%
457
 
3.9%
5821
56.2%
6252
 
17.3%
7205
 
14.0%
872
 
4.9%
922
 
1.5%
ValueCountFrequency (%)
922
 
1.5%
872
 
4.9%
7205
 
14.0%
6252
 
17.3%
5821
56.2%
457
 
3.9%
325
 
1.7%
25
 
0.3%
11
 
0.1%

YearBuilt
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct112
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1971.267808
Minimum1872
Maximum2010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2022-01-26T19:48:01.932959image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1872
5-th percentile1916
Q11954
median1973
Q32000
95-th percentile2007
Maximum2010
Range138
Interquartile range (IQR)46

Descriptive statistics

Standard deviation30.20290404
Coefficient of variation (CV)0.01532156307
Kurtosis-0.4395519416
Mean1971.267808
Median Absolute Deviation (MAD)25
Skewness-0.6134611725
Sum2878051
Variance912.2154126
MonotonicityNot monotonic
2022-01-26T19:48:02.341419image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
200667
 
4.6%
200564
 
4.4%
200454
 
3.7%
200749
 
3.4%
200345
 
3.1%
197633
 
2.3%
197732
 
2.2%
192030
 
2.1%
195926
 
1.8%
199825
 
1.7%
Other values (102)1035
70.9%
ValueCountFrequency (%)
18721
 
0.1%
18751
 
0.1%
18804
 
0.3%
18821
 
0.1%
18852
 
0.1%
18902
 
0.1%
18922
 
0.1%
18931
 
0.1%
18981
 
0.1%
190010
0.7%
ValueCountFrequency (%)
20101
 
0.1%
200918
 
1.2%
200823
 
1.6%
200749
3.4%
200667
4.6%
200564
4.4%
200454
3.7%
200345
3.1%
200223
 
1.6%
200120
 
1.4%

YearRemodAdd
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct61
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1984.865753
Minimum1950
Maximum2010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2022-01-26T19:48:02.663512image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1950
5-th percentile1950
Q11967
median1994
Q32004
95-th percentile2007
Maximum2010
Range60
Interquartile range (IQR)37

Descriptive statistics

Standard deviation20.64540681
Coefficient of variation (CV)0.01040141217
Kurtosis-1.272245192
Mean1984.865753
Median Absolute Deviation (MAD)13
Skewness-0.5035620027
Sum2897904
Variance426.2328223
MonotonicityNot monotonic
2022-01-26T19:48:02.977696image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1950178
 
12.2%
200697
 
6.6%
200776
 
5.2%
200573
 
5.0%
200462
 
4.2%
200055
 
3.8%
200351
 
3.5%
200248
 
3.3%
200840
 
2.7%
199636
 
2.5%
Other values (51)744
51.0%
ValueCountFrequency (%)
1950178
12.2%
19514
 
0.3%
19525
 
0.3%
195310
 
0.7%
195414
 
1.0%
19559
 
0.6%
195610
 
0.7%
19579
 
0.6%
195815
 
1.0%
195918
 
1.2%
ValueCountFrequency (%)
20106
 
0.4%
200923
 
1.6%
200840
2.7%
200776
5.2%
200697
6.6%
200573
5.0%
200462
4.2%
200351
3.5%
200248
3.3%
200121
 
1.4%

MasVnrArea
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct327
Distinct (%)22.5%
Missing8
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean103.6852617
Minimum0
Maximum1600
Zeros861
Zeros (%)59.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2022-01-26T19:48:03.289035image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3166
95-th percentile456
Maximum1600
Range1600
Interquartile range (IQR)166

Descriptive statistics

Standard deviation181.0662066
Coefficient of variation (CV)1.746306115
Kurtosis10.08241732
Mean103.6852617
Median Absolute Deviation (MAD)0
Skewness2.66908421
Sum150551
Variance32784.97117
MonotonicityNot monotonic
2022-01-26T19:48:03.605737image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0861
59.0%
728
 
0.5%
1088
 
0.5%
1808
 
0.5%
1207
 
0.5%
167
 
0.5%
3406
 
0.4%
1066
 
0.4%
806
 
0.4%
2006
 
0.4%
Other values (317)529
36.2%
(Missing)8
 
0.5%
ValueCountFrequency (%)
0861
59.0%
12
 
0.1%
111
 
0.1%
141
 
0.1%
167
 
0.5%
182
 
0.1%
221
 
0.1%
241
 
0.1%
271
 
0.1%
281
 
0.1%
ValueCountFrequency (%)
16001
0.1%
13781
0.1%
11701
0.1%
11291
0.1%
11151
0.1%
10471
0.1%
10311
0.1%
9751
0.1%
9221
0.1%
9211
0.1%

BsmtFinSF1
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct637
Distinct (%)43.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean443.639726
Minimum0
Maximum5644
Zeros467
Zeros (%)32.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2022-01-26T19:48:03.894492image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median383.5
Q3712.25
95-th percentile1274
Maximum5644
Range5644
Interquartile range (IQR)712.25

Descriptive statistics

Standard deviation456.0980908
Coefficient of variation (CV)1.028082167
Kurtosis11.11823629
Mean443.639726
Median Absolute Deviation (MAD)383.5
Skewness1.685503072
Sum647714
Variance208025.4685
MonotonicityNot monotonic
2022-01-26T19:48:04.202965image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0467
32.0%
2412
 
0.8%
169
 
0.6%
6865
 
0.3%
6625
 
0.3%
205
 
0.3%
9365
 
0.3%
6165
 
0.3%
5604
 
0.3%
5534
 
0.3%
Other values (627)939
64.3%
ValueCountFrequency (%)
0467
32.0%
21
 
0.1%
169
 
0.6%
205
 
0.3%
2412
 
0.8%
251
 
0.1%
271
 
0.1%
283
 
0.2%
331
 
0.1%
351
 
0.1%
ValueCountFrequency (%)
56441
0.1%
22601
0.1%
21881
0.1%
20961
0.1%
19041
0.1%
18801
0.1%
18101
0.1%
17671
0.1%
17211
0.1%
16961
0.1%

BsmtFinSF2
Real number (ℝ≥0)

ZEROS

Distinct144
Distinct (%)9.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.54931507
Minimum0
Maximum1474
Zeros1293
Zeros (%)88.6%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2022-01-26T19:48:04.496803image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile396.2
Maximum1474
Range1474
Interquartile range (IQR)0

Descriptive statistics

Standard deviation161.3192728
Coefficient of variation (CV)3.465556315
Kurtosis20.11333755
Mean46.54931507
Median Absolute Deviation (MAD)0
Skewness4.255261109
Sum67962
Variance26023.90778
MonotonicityNot monotonic
2022-01-26T19:48:04.810215image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01293
88.6%
1805
 
0.3%
3743
 
0.2%
5512
 
0.1%
1472
 
0.1%
2942
 
0.1%
3912
 
0.1%
5392
 
0.1%
962
 
0.1%
4802
 
0.1%
Other values (134)145
 
9.9%
ValueCountFrequency (%)
01293
88.6%
281
 
0.1%
321
 
0.1%
351
 
0.1%
401
 
0.1%
412
 
0.1%
642
 
0.1%
681
 
0.1%
801
 
0.1%
811
 
0.1%
ValueCountFrequency (%)
14741
0.1%
11271
0.1%
11201
0.1%
10851
0.1%
10801
0.1%
10631
0.1%
10611
0.1%
10571
0.1%
10311
0.1%
10291
0.1%

BsmtUnfSF
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct780
Distinct (%)53.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean567.240411
Minimum0
Maximum2336
Zeros118
Zeros (%)8.1%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2022-01-26T19:48:05.081354image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1223
median477.5
Q3808
95-th percentile1468
Maximum2336
Range2336
Interquartile range (IQR)585

Descriptive statistics

Standard deviation441.8669553
Coefficient of variation (CV)0.7789765094
Kurtosis0.4749939878
Mean567.240411
Median Absolute Deviation (MAD)288
Skewness0.9202684528
Sum828171
Variance195246.4062
MonotonicityNot monotonic
2022-01-26T19:48:05.385292image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0118
 
8.1%
7289
 
0.6%
3848
 
0.5%
6007
 
0.5%
3007
 
0.5%
5727
 
0.5%
2706
 
0.4%
6256
 
0.4%
6726
 
0.4%
4406
 
0.4%
Other values (770)1280
87.7%
ValueCountFrequency (%)
0118
8.1%
141
 
0.1%
151
 
0.1%
232
 
0.1%
261
 
0.1%
291
 
0.1%
301
 
0.1%
322
 
0.1%
351
 
0.1%
364
 
0.3%
ValueCountFrequency (%)
23361
0.1%
21531
0.1%
21211
0.1%
20461
0.1%
20421
0.1%
20021
0.1%
19691
0.1%
19351
0.1%
19261
0.1%
19071
0.1%

TotalBsmtSF
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct721
Distinct (%)49.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1057.429452
Minimum0
Maximum6110
Zeros37
Zeros (%)2.5%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2022-01-26T19:48:05.673913image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile519.3
Q1795.75
median991.5
Q31298.25
95-th percentile1753
Maximum6110
Range6110
Interquartile range (IQR)502.5

Descriptive statistics

Standard deviation438.7053245
Coefficient of variation (CV)0.4148790481
Kurtosis13.25048328
Mean1057.429452
Median Absolute Deviation (MAD)234.5
Skewness1.524254549
Sum1543847
Variance192462.3617
MonotonicityNot monotonic
2022-01-26T19:48:05.965165image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
037
 
2.5%
86435
 
2.4%
67217
 
1.2%
91215
 
1.0%
104014
 
1.0%
81613
 
0.9%
76812
 
0.8%
72812
 
0.8%
89411
 
0.8%
78011
 
0.8%
Other values (711)1283
87.9%
ValueCountFrequency (%)
037
2.5%
1051
 
0.1%
1901
 
0.1%
2643
 
0.2%
2701
 
0.1%
2901
 
0.1%
3191
 
0.1%
3601
 
0.1%
3721
 
0.1%
3847
 
0.5%
ValueCountFrequency (%)
61101
0.1%
32061
0.1%
32001
0.1%
31381
0.1%
30941
0.1%
26331
0.1%
25241
0.1%
24441
0.1%
23961
0.1%
23921
0.1%

1stFlrSF
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct753
Distinct (%)51.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1162.626712
Minimum334
Maximum4692
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2022-01-26T19:48:06.275174image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum334
5-th percentile672.95
Q1882
median1087
Q31391.25
95-th percentile1831.25
Maximum4692
Range4358
Interquartile range (IQR)509.25

Descriptive statistics

Standard deviation386.587738
Coefficient of variation (CV)0.3325123481
Kurtosis5.745841482
Mean1162.626712
Median Absolute Deviation (MAD)234.5
Skewness1.376756622
Sum1697435
Variance149450.0792
MonotonicityNot monotonic
2022-01-26T19:48:06.562982image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
86425
 
1.7%
104016
 
1.1%
91214
 
1.0%
89412
 
0.8%
84812
 
0.8%
67211
 
0.8%
6309
 
0.6%
8169
 
0.6%
4837
 
0.5%
9607
 
0.5%
Other values (743)1338
91.6%
ValueCountFrequency (%)
3341
 
0.1%
3721
 
0.1%
4381
 
0.1%
4801
 
0.1%
4837
0.5%
4951
 
0.1%
5205
0.3%
5251
 
0.1%
5261
 
0.1%
5361
 
0.1%
ValueCountFrequency (%)
46921
0.1%
32281
0.1%
31381
0.1%
28981
0.1%
26331
0.1%
25241
0.1%
25151
0.1%
24441
0.1%
24111
0.1%
24021
0.1%

2ndFlrSF
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct417
Distinct (%)28.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean346.9924658
Minimum0
Maximum2065
Zeros829
Zeros (%)56.8%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2022-01-26T19:48:06.855413image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3728
95-th percentile1141.05
Maximum2065
Range2065
Interquartile range (IQR)728

Descriptive statistics

Standard deviation436.5284359
Coefficient of variation (CV)1.258034335
Kurtosis-0.5534635576
Mean346.9924658
Median Absolute Deviation (MAD)0
Skewness0.8130298163
Sum506609
Variance190557.0753
MonotonicityNot monotonic
2022-01-26T19:48:07.157965image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0829
56.8%
72810
 
0.7%
5049
 
0.6%
5468
 
0.5%
6728
 
0.5%
6007
 
0.5%
7207
 
0.5%
8966
 
0.4%
8625
 
0.3%
7805
 
0.3%
Other values (407)566
38.8%
ValueCountFrequency (%)
0829
56.8%
1101
 
0.1%
1671
 
0.1%
1921
 
0.1%
2081
 
0.1%
2131
 
0.1%
2201
 
0.1%
2241
 
0.1%
2402
 
0.1%
2522
 
0.1%
ValueCountFrequency (%)
20651
0.1%
18721
0.1%
18181
0.1%
17961
0.1%
16111
0.1%
15891
0.1%
15401
0.1%
15381
0.1%
15231
0.1%
15191
0.1%

LowQualFinSF
Real number (ℝ≥0)

ZEROS

Distinct24
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.844520548
Minimum0
Maximum572
Zeros1434
Zeros (%)98.2%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2022-01-26T19:48:07.404151image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum572
Range572
Interquartile range (IQR)0

Descriptive statistics

Standard deviation48.62308143
Coefficient of variation (CV)8.319430317
Kurtosis83.23481667
Mean5.844520548
Median Absolute Deviation (MAD)0
Skewness9.011341288
Sum8533
Variance2364.204048
MonotonicityNot monotonic
2022-01-26T19:48:07.676461image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
01434
98.2%
803
 
0.2%
3602
 
0.1%
2051
 
0.1%
4791
 
0.1%
3971
 
0.1%
5141
 
0.1%
1201
 
0.1%
4811
 
0.1%
2321
 
0.1%
Other values (14)14
 
1.0%
ValueCountFrequency (%)
01434
98.2%
531
 
0.1%
803
 
0.2%
1201
 
0.1%
1441
 
0.1%
1561
 
0.1%
2051
 
0.1%
2321
 
0.1%
2341
 
0.1%
3602
 
0.1%
ValueCountFrequency (%)
5721
0.1%
5281
0.1%
5151
0.1%
5141
0.1%
5131
0.1%
4811
0.1%
4791
0.1%
4731
0.1%
4201
0.1%
3971
0.1%

GrLivArea
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct861
Distinct (%)59.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1515.463699
Minimum334
Maximum5642
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2022-01-26T19:48:07.929992image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum334
5-th percentile848
Q11129.5
median1464
Q31776.75
95-th percentile2466.1
Maximum5642
Range5308
Interquartile range (IQR)647.25

Descriptive statistics

Standard deviation525.4803834
Coefficient of variation (CV)0.3467456092
Kurtosis4.895120581
Mean1515.463699
Median Absolute Deviation (MAD)326
Skewness1.366560356
Sum2212577
Variance276129.6334
MonotonicityNot monotonic
2022-01-26T19:48:08.243088image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
86422
 
1.5%
104014
 
1.0%
89411
 
0.8%
145610
 
0.7%
84810
 
0.7%
12009
 
0.6%
9129
 
0.6%
8168
 
0.5%
10928
 
0.5%
17287
 
0.5%
Other values (851)1352
92.6%
ValueCountFrequency (%)
3341
 
0.1%
4381
 
0.1%
4801
 
0.1%
5201
 
0.1%
6051
 
0.1%
6161
 
0.1%
6306
0.4%
6722
 
0.1%
6911
 
0.1%
6931
 
0.1%
ValueCountFrequency (%)
56421
0.1%
46761
0.1%
44761
0.1%
43161
0.1%
36271
0.1%
36081
0.1%
34931
0.1%
34471
0.1%
33951
0.1%
32791
0.1%

BsmtFullBath
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
856 
1
588 
2
 
15
3
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row1
2nd row0
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0856
58.6%
1588
40.3%
215
 
1.0%
31
 
0.1%

Length

2022-01-26T19:48:08.739055image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-26T19:48:08.892297image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0856
58.6%
1588
40.3%
215
 
1.0%
31
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0856
58.6%
1588
40.3%
215
 
1.0%
31
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0856
58.6%
1588
40.3%
215
 
1.0%
31
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0856
58.6%
1588
40.3%
215
 
1.0%
31
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0856
58.6%
1588
40.3%
215
 
1.0%
31
 
0.1%

BsmtHalfBath
Categorical

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
1378 
1
 
80
2
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
01378
94.4%
180
 
5.5%
22
 
0.1%

Length

2022-01-26T19:48:09.314334image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-26T19:48:09.466815image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
01378
94.4%
180
 
5.5%
22
 
0.1%

Most occurring characters

ValueCountFrequency (%)
01378
94.4%
180
 
5.5%
22
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01378
94.4%
180
 
5.5%
22
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01378
94.4%
180
 
5.5%
22
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01378
94.4%
180
 
5.5%
22
 
0.1%

FullBath
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
2
768 
1
650 
3
 
33
0
 
9

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row1
5th row2

Common Values

ValueCountFrequency (%)
2768
52.6%
1650
44.5%
333
 
2.3%
09
 
0.6%

Length

2022-01-26T19:48:09.913592image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-26T19:48:10.075123image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
2768
52.6%
1650
44.5%
333
 
2.3%
09
 
0.6%

Most occurring characters

ValueCountFrequency (%)
2768
52.6%
1650
44.5%
333
 
2.3%
09
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2768
52.6%
1650
44.5%
333
 
2.3%
09
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2768
52.6%
1650
44.5%
333
 
2.3%
09
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2768
52.6%
1650
44.5%
333
 
2.3%
09
 
0.6%

HalfBath
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
913 
1
535 
2
 
12

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0913
62.5%
1535
36.6%
212
 
0.8%

Length

2022-01-26T19:48:10.461292image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-26T19:48:10.623634image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0913
62.5%
1535
36.6%
212
 
0.8%

Most occurring characters

ValueCountFrequency (%)
0913
62.5%
1535
36.6%
212
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0913
62.5%
1535
36.6%
212
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0913
62.5%
1535
36.6%
212
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0913
62.5%
1535
36.6%
212
 
0.8%

BedroomAbvGr
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct8
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.866438356
Minimum0
Maximum8
Zeros6
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2022-01-26T19:48:10.754346image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q12
median3
Q33
95-th percentile4
Maximum8
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.8157780441
Coefficient of variation (CV)0.2845964025
Kurtosis2.230874582
Mean2.866438356
Median Absolute Deviation (MAD)0
Skewness0.2117900963
Sum4185
Variance0.6654938173
MonotonicityNot monotonic
2022-01-26T19:48:11.004926image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
3804
55.1%
2358
24.5%
4213
 
14.6%
150
 
3.4%
521
 
1.4%
67
 
0.5%
06
 
0.4%
81
 
0.1%
ValueCountFrequency (%)
06
 
0.4%
150
 
3.4%
2358
24.5%
3804
55.1%
4213
 
14.6%
521
 
1.4%
67
 
0.5%
81
 
0.1%
ValueCountFrequency (%)
81
 
0.1%
67
 
0.5%
521
 
1.4%
4213
 
14.6%
3804
55.1%
2358
24.5%
150
 
3.4%
06
 
0.4%

KitchenAbvGr
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
1
1392 
2
 
65
3
 
2
0
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
11392
95.3%
265
 
4.5%
32
 
0.1%
01
 
0.1%

Length

2022-01-26T19:48:11.533653image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-26T19:48:11.698876image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
11392
95.3%
265
 
4.5%
32
 
0.1%
01
 
0.1%

Most occurring characters

ValueCountFrequency (%)
11392
95.3%
265
 
4.5%
32
 
0.1%
01
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
11392
95.3%
265
 
4.5%
32
 
0.1%
01
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
11392
95.3%
265
 
4.5%
32
 
0.1%
01
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
11392
95.3%
265
 
4.5%
32
 
0.1%
01
 
0.1%

TotRmsAbvGrd
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct12
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.517808219
Minimum2
Maximum14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2022-01-26T19:48:11.816266image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4
Q15
median6
Q37
95-th percentile10
Maximum14
Range12
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.625393291
Coefficient of variation (CV)0.2493772808
Kurtosis0.8807615657
Mean6.517808219
Median Absolute Deviation (MAD)1
Skewness0.6763408364
Sum9516
Variance2.641903349
MonotonicityNot monotonic
2022-01-26T19:48:12.076228image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
6402
27.5%
7329
22.5%
5275
18.8%
8187
12.8%
497
 
6.6%
975
 
5.1%
1047
 
3.2%
1118
 
1.2%
317
 
1.2%
1211
 
0.8%
Other values (2)2
 
0.1%
ValueCountFrequency (%)
21
 
0.1%
317
 
1.2%
497
 
6.6%
5275
18.8%
6402
27.5%
7329
22.5%
8187
12.8%
975
 
5.1%
1047
 
3.2%
1118
 
1.2%
ValueCountFrequency (%)
141
 
0.1%
1211
 
0.8%
1118
 
1.2%
1047
 
3.2%
975
 
5.1%
8187
12.8%
7329
22.5%
6402
27.5%
5275
18.8%
497
 
6.6%

Fireplaces
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
690 
1
650 
2
115 
3
 
5

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0690
47.3%
1650
44.5%
2115
 
7.9%
35
 
0.3%

Length

2022-01-26T19:48:12.654871image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-26T19:48:12.813571image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0690
47.3%
1650
44.5%
2115
 
7.9%
35
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0690
47.3%
1650
44.5%
2115
 
7.9%
35
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0690
47.3%
1650
44.5%
2115
 
7.9%
35
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0690
47.3%
1650
44.5%
2115
 
7.9%
35
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0690
47.3%
1650
44.5%
2115
 
7.9%
35
 
0.3%

GarageYrBlt
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct97
Distinct (%)7.0%
Missing81
Missing (%)5.5%
Infinite0
Infinite (%)0.0%
Mean1978.506164
Minimum1900
Maximum2010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2022-01-26T19:48:13.063583image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1900
5-th percentile1930
Q11961
median1980
Q32002
95-th percentile2007
Maximum2010
Range110
Interquartile range (IQR)41

Descriptive statistics

Standard deviation24.68972477
Coefficient of variation (CV)0.01247897288
Kurtosis-0.418340998
Mean1978.506164
Median Absolute Deviation (MAD)21
Skewness-0.6494146239
Sum2728360
Variance609.5825091
MonotonicityNot monotonic
2022-01-26T19:48:13.430710image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
200565
 
4.5%
200659
 
4.0%
200453
 
3.6%
200350
 
3.4%
200749
 
3.4%
197735
 
2.4%
199831
 
2.1%
199930
 
2.1%
197629
 
2.0%
200829
 
2.0%
Other values (87)949
65.0%
(Missing)81
 
5.5%
ValueCountFrequency (%)
19001
 
0.1%
19061
 
0.1%
19081
 
0.1%
19103
 
0.2%
19142
 
0.1%
19152
 
0.1%
19165
 
0.3%
19182
 
0.1%
192014
1.0%
19213
 
0.2%
ValueCountFrequency (%)
20103
 
0.2%
200921
 
1.4%
200829
2.0%
200749
3.4%
200659
4.0%
200565
4.5%
200453
3.6%
200350
3.4%
200226
 
1.8%
200120
 
1.4%

GarageCars
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
2
824 
1
369 
3
181 
0
 
81
4
 
5

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row3
5th row3

Common Values

ValueCountFrequency (%)
2824
56.4%
1369
25.3%
3181
 
12.4%
081
 
5.5%
45
 
0.3%

Length

2022-01-26T19:48:13.985623image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-26T19:48:14.161838image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
2824
56.4%
1369
25.3%
3181
 
12.4%
081
 
5.5%
45
 
0.3%

Most occurring characters

ValueCountFrequency (%)
2824
56.4%
1369
25.3%
3181
 
12.4%
081
 
5.5%
45
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2824
56.4%
1369
25.3%
3181
 
12.4%
081
 
5.5%
45
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2824
56.4%
1369
25.3%
3181
 
12.4%
081
 
5.5%
45
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2824
56.4%
1369
25.3%
3181
 
12.4%
081
 
5.5%
45
 
0.3%

GarageArea
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct441
Distinct (%)30.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean472.980137
Minimum0
Maximum1418
Zeros81
Zeros (%)5.5%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2022-01-26T19:48:14.328989image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1334.5
median480
Q3576
95-th percentile850.1
Maximum1418
Range1418
Interquartile range (IQR)241.5

Descriptive statistics

Standard deviation213.8048415
Coefficient of variation (CV)0.452037675
Kurtosis0.9170672023
Mean472.980137
Median Absolute Deviation (MAD)120
Skewness0.1799809067
Sum690551
Variance45712.51023
MonotonicityNot monotonic
2022-01-26T19:48:14.667487image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
081
 
5.5%
44049
 
3.4%
57647
 
3.2%
24038
 
2.6%
48434
 
2.3%
52833
 
2.3%
28827
 
1.8%
40025
 
1.7%
26424
 
1.6%
48024
 
1.6%
Other values (431)1078
73.8%
ValueCountFrequency (%)
081
5.5%
1602
 
0.1%
1641
 
0.1%
1809
 
0.6%
1861
 
0.1%
1891
 
0.1%
1921
 
0.1%
1981
 
0.1%
2004
 
0.3%
2053
 
0.2%
ValueCountFrequency (%)
14181
0.1%
13901
0.1%
13561
0.1%
12481
0.1%
12201
0.1%
11661
0.1%
11341
0.1%
10691
0.1%
10531
0.1%
10522
0.1%

WoodDeckSF
Real number (ℝ≥0)

ZEROS

Distinct274
Distinct (%)18.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean94.24452055
Minimum0
Maximum857
Zeros761
Zeros (%)52.1%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2022-01-26T19:48:14.969812image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3168
95-th percentile335
Maximum857
Range857
Interquartile range (IQR)168

Descriptive statistics

Standard deviation125.3387944
Coefficient of variation (CV)1.329931901
Kurtosis2.992950925
Mean94.24452055
Median Absolute Deviation (MAD)0
Skewness1.541375757
Sum137597
Variance15709.81337
MonotonicityNot monotonic
2022-01-26T19:48:15.334314image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0761
52.1%
19238
 
2.6%
10036
 
2.5%
14433
 
2.3%
12031
 
2.1%
16828
 
1.9%
14015
 
1.0%
22414
 
1.0%
20810
 
0.7%
24010
 
0.7%
Other values (264)484
33.2%
ValueCountFrequency (%)
0761
52.1%
122
 
0.1%
242
 
0.1%
262
 
0.1%
282
 
0.1%
301
 
0.1%
321
 
0.1%
331
 
0.1%
351
 
0.1%
364
 
0.3%
ValueCountFrequency (%)
8571
0.1%
7361
0.1%
7281
0.1%
6701
0.1%
6681
0.1%
6351
0.1%
5861
0.1%
5761
0.1%
5741
0.1%
5501
0.1%

OpenPorchSF
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct202
Distinct (%)13.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.66027397
Minimum0
Maximum547
Zeros656
Zeros (%)44.9%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2022-01-26T19:48:15.617990image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median25
Q368
95-th percentile175.05
Maximum547
Range547
Interquartile range (IQR)68

Descriptive statistics

Standard deviation66.25602768
Coefficient of variation (CV)1.419966538
Kurtosis8.490335806
Mean46.66027397
Median Absolute Deviation (MAD)25
Skewness2.36434174
Sum68124
Variance4389.861203
MonotonicityNot monotonic
2022-01-26T19:48:15.958978image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0656
44.9%
3629
 
2.0%
4822
 
1.5%
2021
 
1.4%
4019
 
1.3%
4519
 
1.3%
2416
 
1.1%
3016
 
1.1%
6015
 
1.0%
3914
 
1.0%
Other values (192)633
43.4%
ValueCountFrequency (%)
0656
44.9%
41
 
0.1%
81
 
0.1%
101
 
0.1%
111
 
0.1%
123
 
0.2%
151
 
0.1%
168
 
0.5%
172
 
0.1%
185
 
0.3%
ValueCountFrequency (%)
5471
0.1%
5231
0.1%
5021
0.1%
4181
0.1%
4061
0.1%
3641
0.1%
3411
0.1%
3191
0.1%
3122
0.1%
3041
0.1%

EnclosedPorch
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct120
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.95410959
Minimum0
Maximum552
Zeros1252
Zeros (%)85.8%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2022-01-26T19:48:16.237377image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile180.15
Maximum552
Range552
Interquartile range (IQR)0

Descriptive statistics

Standard deviation61.1191486
Coefficient of variation (CV)2.783950237
Kurtosis10.43076594
Mean21.95410959
Median Absolute Deviation (MAD)0
Skewness3.089871904
Sum32053
Variance3735.550326
MonotonicityNot monotonic
2022-01-26T19:48:16.587879image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01252
85.8%
11215
 
1.0%
966
 
0.4%
1925
 
0.3%
1445
 
0.3%
1205
 
0.3%
2165
 
0.3%
1564
 
0.3%
1164
 
0.3%
2524
 
0.3%
Other values (110)155
 
10.6%
ValueCountFrequency (%)
01252
85.8%
191
 
0.1%
201
 
0.1%
241
 
0.1%
301
 
0.1%
322
 
0.1%
342
 
0.1%
362
 
0.1%
371
 
0.1%
392
 
0.1%
ValueCountFrequency (%)
5521
0.1%
3861
0.1%
3301
0.1%
3181
0.1%
3011
0.1%
2941
0.1%
2931
0.1%
2911
0.1%
2861
0.1%
2801
0.1%

3SsnPorch
Real number (ℝ≥0)

ZEROS

Distinct20
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.409589041
Minimum0
Maximum508
Zeros1436
Zeros (%)98.4%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2022-01-26T19:48:18.128163image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum508
Range508
Interquartile range (IQR)0

Descriptive statistics

Standard deviation29.31733056
Coefficient of variation (CV)8.598493896
Kurtosis123.6623794
Mean3.409589041
Median Absolute Deviation (MAD)0
Skewness10.30434203
Sum4978
Variance859.505871
MonotonicityNot monotonic
2022-01-26T19:48:18.422104image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
01436
98.4%
1683
 
0.2%
1442
 
0.1%
1802
 
0.1%
2162
 
0.1%
2901
 
0.1%
1531
 
0.1%
961
 
0.1%
231
 
0.1%
1621
 
0.1%
Other values (10)10
 
0.7%
ValueCountFrequency (%)
01436
98.4%
231
 
0.1%
961
 
0.1%
1301
 
0.1%
1401
 
0.1%
1442
 
0.1%
1531
 
0.1%
1621
 
0.1%
1683
 
0.2%
1802
 
0.1%
ValueCountFrequency (%)
5081
0.1%
4071
0.1%
3201
0.1%
3041
0.1%
2901
0.1%
2451
0.1%
2381
0.1%
2162
0.1%
1961
0.1%
1821
0.1%

ScreenPorch
Real number (ℝ≥0)

ZEROS

Distinct76
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.0609589
Minimum0
Maximum480
Zeros1344
Zeros (%)92.1%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2022-01-26T19:48:18.734047image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile160
Maximum480
Range480
Interquartile range (IQR)0

Descriptive statistics

Standard deviation55.75741528
Coefficient of variation (CV)3.70211589
Kurtosis18.43906784
Mean15.0609589
Median Absolute Deviation (MAD)0
Skewness4.122213743
Sum21989
Variance3108.889359
MonotonicityNot monotonic
2022-01-26T19:48:19.090665image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01344
92.1%
1926
 
0.4%
1205
 
0.3%
2245
 
0.3%
1894
 
0.3%
1804
 
0.3%
1473
 
0.2%
903
 
0.2%
1603
 
0.2%
1443
 
0.2%
Other values (66)80
 
5.5%
ValueCountFrequency (%)
01344
92.1%
401
 
0.1%
531
 
0.1%
601
 
0.1%
631
 
0.1%
801
 
0.1%
903
 
0.2%
951
 
0.1%
991
 
0.1%
1002
 
0.1%
ValueCountFrequency (%)
4801
0.1%
4401
0.1%
4101
0.1%
3961
0.1%
3851
0.1%
3741
0.1%
3221
0.1%
3121
0.1%
2911
0.1%
2882
0.1%

PoolArea
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct8
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.75890411
Minimum0
Maximum738
Zeros1453
Zeros (%)99.5%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2022-01-26T19:48:19.360605image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum738
Range738
Interquartile range (IQR)0

Descriptive statistics

Standard deviation40.17730694
Coefficient of variation (CV)14.56277759
Kurtosis223.2684989
Mean2.75890411
Median Absolute Deviation (MAD)0
Skewness14.82837364
Sum4028
Variance1614.215993
MonotonicityNot monotonic
2022-01-26T19:48:19.608720image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
01453
99.5%
5121
 
0.1%
6481
 
0.1%
5761
 
0.1%
5551
 
0.1%
4801
 
0.1%
5191
 
0.1%
7381
 
0.1%
ValueCountFrequency (%)
01453
99.5%
4801
 
0.1%
5121
 
0.1%
5191
 
0.1%
5551
 
0.1%
5761
 
0.1%
6481
 
0.1%
7381
 
0.1%
ValueCountFrequency (%)
7381
 
0.1%
6481
 
0.1%
5761
 
0.1%
5551
 
0.1%
5191
 
0.1%
5121
 
0.1%
4801
 
0.1%
01453
99.5%

MiscVal
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct21
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.4890411
Minimum0
Maximum15500
Zeros1408
Zeros (%)96.4%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2022-01-26T19:48:19.885022image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum15500
Range15500
Interquartile range (IQR)0

Descriptive statistics

Standard deviation496.1230245
Coefficient of variation (CV)11.408001
Kurtosis701.0033423
Mean43.4890411
Median Absolute Deviation (MAD)0
Skewness24.47679419
Sum63494
Variance246138.0554
MonotonicityNot monotonic
2022-01-26T19:48:20.196084image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
01408
96.4%
40011
 
0.8%
5008
 
0.5%
7005
 
0.3%
4504
 
0.3%
6004
 
0.3%
20004
 
0.3%
12002
 
0.1%
4802
 
0.1%
155001
 
0.1%
Other values (11)11
 
0.8%
ValueCountFrequency (%)
01408
96.4%
541
 
0.1%
3501
 
0.1%
40011
 
0.8%
4504
 
0.3%
4802
 
0.1%
5008
 
0.5%
5601
 
0.1%
6004
 
0.3%
6201
 
0.1%
ValueCountFrequency (%)
155001
 
0.1%
83001
 
0.1%
35001
 
0.1%
25001
 
0.1%
20004
0.3%
14001
 
0.1%
13001
 
0.1%
12002
0.1%
11501
 
0.1%
8001
 
0.1%

MoSold
Real number (ℝ≥0)

Distinct12
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.321917808
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2022-01-26T19:48:20.507224image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q15
median6
Q38
95-th percentile11
Maximum12
Range11
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.703626208
Coefficient of variation (CV)0.4276591836
Kurtosis-0.4041093415
Mean6.321917808
Median Absolute Deviation (MAD)2
Skewness0.2120529851
Sum9230
Variance7.309594675
MonotonicityNot monotonic
2022-01-26T19:48:20.785342image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
6253
17.3%
7234
16.0%
5204
14.0%
4141
9.7%
8122
8.4%
3106
7.3%
1089
 
6.1%
1179
 
5.4%
963
 
4.3%
1259
 
4.0%
Other values (2)110
7.5%
ValueCountFrequency (%)
158
 
4.0%
252
 
3.6%
3106
7.3%
4141
9.7%
5204
14.0%
6253
17.3%
7234
16.0%
8122
8.4%
963
 
4.3%
1089
 
6.1%
ValueCountFrequency (%)
1259
 
4.0%
1179
 
5.4%
1089
 
6.1%
963
 
4.3%
8122
8.4%
7234
16.0%
6253
17.3%
5204
14.0%
4141
9.7%
3106
7.3%

YrSold
Categorical

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size87.1 KiB
2009
338 
2007
329 
2006
314 
2008
304 
2010
175 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters5840
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2008
2nd row2007
3rd row2008
4th row2006
5th row2008

Common Values

ValueCountFrequency (%)
2009338
23.2%
2007329
22.5%
2006314
21.5%
2008304
20.8%
2010175
12.0%

Length

2022-01-26T19:48:21.364544image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-26T19:48:21.553573image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
2009338
23.2%
2007329
22.5%
2006314
21.5%
2008304
20.8%
2010175
12.0%

Most occurring characters

ValueCountFrequency (%)
02920
50.0%
21460
25.0%
9338
 
5.8%
7329
 
5.6%
6314
 
5.4%
8304
 
5.2%
1175
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number5840
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
02920
50.0%
21460
25.0%
9338
 
5.8%
7329
 
5.6%
6314
 
5.4%
8304
 
5.2%
1175
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
Common5840
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
02920
50.0%
21460
25.0%
9338
 
5.8%
7329
 
5.6%
6314
 
5.4%
8304
 
5.2%
1175
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII5840
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
02920
50.0%
21460
25.0%
9338
 
5.8%
7329
 
5.6%
6314
 
5.4%
8304
 
5.2%
1175
 
3.0%

SalePrice
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct663
Distinct (%)45.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean180921.1959
Minimum34900
Maximum755000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2022-01-26T19:48:21.836966image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum34900
5-th percentile88000
Q1129975
median163000
Q3214000
95-th percentile326100
Maximum755000
Range720100
Interquartile range (IQR)84025

Descriptive statistics

Standard deviation79442.50288
Coefficient of variation (CV)0.4391000319
Kurtosis6.53628186
Mean180921.1959
Median Absolute Deviation (MAD)38000
Skewness1.88287576
Sum264144946
Variance6311111264
MonotonicityNot monotonic
2022-01-26T19:48:22.217405image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14000020
 
1.4%
13500017
 
1.2%
15500014
 
1.0%
14500014
 
1.0%
19000013
 
0.9%
11000013
 
0.9%
11500012
 
0.8%
16000012
 
0.8%
13000011
 
0.8%
13900011
 
0.8%
Other values (653)1323
90.6%
ValueCountFrequency (%)
349001
0.1%
353111
0.1%
379001
0.1%
393001
0.1%
400001
0.1%
520001
0.1%
525001
0.1%
550002
0.1%
559931
0.1%
585001
0.1%
ValueCountFrequency (%)
7550001
0.1%
7450001
0.1%
6250001
0.1%
6116571
0.1%
5829331
0.1%
5565811
0.1%
5550001
0.1%
5380001
0.1%
5018371
0.1%
4850001
0.1%

Interactions

2022-01-26T19:44:24.936457image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-26T19:44:25.185977image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-26T19:44:25.408812image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-26T19:44:25.645613image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-26T19:44:25.862755image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-26T19:44:26.085133image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-26T19:44:26.304961image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-26T19:44:26.526345image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-26T19:44:26.752146image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-26T19:44:26.990546image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-26T19:44:27.201798image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-26T19:44:27.431551image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-26T19:44:27.660655image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-26T19:44:27.887193image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-26T19:44:28.103815image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-26T19:44:28.313077image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-26T19:44:28.535405image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-26T19:44:28.744225image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-26T19:44:28.966443image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-26T19:44:29.181738image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-26T19:44:29.393981image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-26T19:44:29.635977image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-26T19:44:29.843484image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-26T19:44:30.053653image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-26T19:44:30.322882image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-26T19:44:30.594896image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-26T19:44:30.865117image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-26T19:44:31.753852image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-26T19:44:31.981522image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-26T19:44:32.203094image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-26T19:44:32.426888image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-26T19:44:32.689291image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-26T19:44:32.910790image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-26T19:44:33.194755image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-26T19:44:33.472087image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-26T19:44:33.744388image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-26T19:44:33.989344image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-26T19:44:34.209733image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-26T19:44:34.435186image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-26T19:44:34.657176image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-26T19:44:34.868127image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-26T19:44:35.095011image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-26T19:44:35.294020image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-26T19:44:35.509103image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-26T19:44:35.731065image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-26T19:44:35.943762image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-26T19:44:36.160748image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-26T19:44:36.373930image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-26T19:44:36.596909image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-26T19:44:36.808286image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-26T19:44:37.048079image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-26T19:44:37.263587image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-26T19:44:37.466909image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-26T19:44:37.685725image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-26T19:44:37.891267image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-26T19:44:38.122082image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-26T19:44:38.333631image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-26T19:44:38.550961image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-26T19:44:38.789925image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-26T19:44:39.003115image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-26T19:44:39.239547image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-26T19:44:39.461738image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-26T19:44:39.690453image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-26T19:44:39.891844image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-26T19:44:40.092789image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-26T19:44:40.292510image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-26T19:44:40.512628image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-26T19:44:40.738902image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-26T19:44:40.952340image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-26T19:44:41.184071image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-26T19:44:41.387464image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-26T19:44:41.615147image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-26T19:44:41.828329image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-26T19:44:42.044776image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-26T19:44:42.251605image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-26T19:44:42.461634image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-26T19:44:42.676077image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-26T19:44:42.891964image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-26T19:44:43.112083image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-26T19:44:43.324289image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-01-26T19:47:46.251410image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-26T19:47:46.460644image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-26T19:47:46.680947image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-26T19:47:47.008787image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-26T19:47:47.231202image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-26T19:47:47.421739image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-26T19:47:47.608157image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-26T19:47:48.023999image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-26T19:47:48.737268image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-26T19:47:49.445021image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-26T19:47:49.802778image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-26T19:47:50.019417image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-26T19:47:50.673502image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-26T19:47:50.876889image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-26T19:47:51.120033image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-26T19:47:51.868843image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-26T19:47:52.541001image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-26T19:47:52.794586image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-26T19:47:53.018624image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-26T19:47:53.654515image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-26T19:47:54.044472image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-01-26T19:48:22.652939image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-01-26T19:48:23.548252image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-01-26T19:48:24.342749image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-01-26T19:48:25.167746image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2022-01-26T19:48:25.776212image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2022-01-26T19:47:54.646536image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-01-26T19:47:57.276358image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-01-26T19:47:57.859647image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-01-26T19:47:58.065700image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

IdMSSubClassLotFrontageLotAreaOverallQualOverallCondYearBuiltYearRemodAddMasVnrAreaBsmtFinSF1BsmtFinSF2BsmtUnfSFTotalBsmtSF1stFlrSF2ndFlrSFLowQualFinSFGrLivAreaBsmtFullBathBsmtHalfBathFullBathHalfBathBedroomAbvGrKitchenAbvGrTotRmsAbvGrdFireplacesGarageYrBltGarageCarsGarageAreaWoodDeckSFOpenPorchSFEnclosedPorch3SsnPorchScreenPorchPoolAreaMiscValMoSoldYrSoldSalePrice
016065.084507520032003196.0706015085685685401710102131802003.025480610000022008208500
122080.0960068197619760.0978028412621262001262012031611976.0246029800000052007181500
236068.0112507520012002162.0486043492092086601786102131612001.026080420000092008223500
347060.0955075191519700.0216054075696175601717101031711998.03642035272000022006140000
456084.0142608520002000350.0655049011451145105302198102141912000.038361928400000122008250000
565085.01411555199319950.073206479679656601362101111501993.024804030032000700102009143000
672075.0100848520042005186.01369031716861694001694102031712004.02636255570000082007307000
7860NaN103827619731973240.0859322161107110798302090102131721973.02484235204228000350112009200000
895051.0612075193119500.000952952102275201774002022821931.02468900205000042008129900
91019050.0742056193919500.085101409911077001077101022521939.01205040000012008118000

Last rows

IdMSSubClassLotFrontageLotAreaOverallQualOverallCondYearBuiltYearRemodAddMasVnrAreaBsmtFinSF1BsmtFinSF2BsmtUnfSFTotalBsmtSF1stFlrSF2ndFlrSFLowQualFinSFGrLivAreaBsmtFullBathBsmtHalfBathFullBathHalfBathBedroomAbvGrKitchenAbvGrTotRmsAbvGrdFireplacesGarageYrBltGarageCarsGarageAreaWoodDeckSFOpenPorchSFEnclosedPorch3SsnPorchScreenPorchPoolAreaMiscValMoSoldYrSoldSalePrice
145014519060.0900055197419740.0008968968968960179200224280NaN0032450000092009136000
145114522078.092628520082009194.000157315731578001578002031712008.038400360000052009287090
1452145318035.03675552005200580.0547005471072001072101021502005.025250280000052006145000
145314542090.01721755200620060.00011401140114000114000103160NaN003656000007200684500
145414552062.0750075200420050.0410081112211221001221102021602004.02400011300000102009185000
145514566062.0791765199920000.00095395395369401647002131711999.024600400000082007175000
145614572085.0131756619781988119.079016358915422073002073102031721978.0250034900000022010210000
145714587066.0904279194120060.0275087711521188115202340002041921941.012520600000250052010266500
145814592068.0971756195019960.0491029010781078001078101021501950.012403660112000042010142125
145914602075.0993756196519650.083029013612561256001256101131601965.01276736680000062008147500